| from sklearn.metrics.pairwise import cosine_similarity |
| from sentence_transformers import SentenceTransformer |
| import datasets |
| import gradio as gr |
|
|
| model = SentenceTransformer('clip-ViT-B-16') |
| dataset = datasets.load_dataset('tadeyina/celeb-identities') |
|
|
| def predict(im1, im2): |
| |
| embeddings = model.encode([im1, im2]) |
| sim = cosine_similarity(embeddings) |
| sim = sim[0, 1] |
| if sim > 0.8: |
| return sim, "SAME PERSON, UNLOCK PHONE" |
| else: |
| return sim, "DIFFERENT PEOPLE, DON'T UNLOCK" |
|
|
|
|
| interface = gr.Interface(fn=predict, |
| inputs= [gr.Image(value = dataset['train']['image'][0], type="pil", source="webcam"), |
| gr.Image(value = dataset['train']['image'][1], type="pil", source="webcam")], |
| outputs= [gr.Number(label="Similarity"), |
| gr.Textbox(label="Message")], |
| title = 'Face ID', |
| description = 'This app uses emage embeddings and cosine similarity to function as a Face ID application. Cosine similarity is used, so it ranges from -1 to 1.' |
| ) |
|
|
| interface.launch(debug=True) |
|
|
